From Origami to Bistable and Laminate Structures: A Review for Multifunctional Applications from Structural Perspective of Shape‐Changing Structures
Bibliographic record
Abstract
Conventional systems based on traditional design strategies typically excel at single‐task performance but lack adaptability when operating conditions change. Reconfiguration offers a promising alternative, enabling systems to adopt multiple configurations tailored to varying requirements. Natural biological organisms regularly modify their morphology to overcome environmental challenges, inspiring engineering applications that seek similar adaptability. However, the real potential of reconfiguration in engineering is often bounded by traditional design strategies and rigid materials. In this case, shape‐changing structures can provide new insights. This review focuses on the structural foundations of reconfigurable design, emphasizing key principles across origami, bistable structures, and laminate structures, and examines how these shape‐changing structures can enhance the multifunctionality in soft robotics, soft manipulators, and metamaterials. Finally, the review discusses the primary challenges faced by achieving the multifunctionality in practical applications. In conclusion, combining advanced materials with innovative structural designs enables systems to achieve diverse working modes and adaptive properties, paving the way for more versatile and resilient applications across various fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".